WP/15/140

IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Assessing Default Risks for Chinese Firms: A Lost Cause?

Daniel Law and Shaun K. Roache

© 2015 International Monetary Fund WP/15/140

IMF Working Paper

Monetary and Capital Markets Department

Assessing Default Risks for Chinese Firms: A Lost Cause?

Prepared by Daniel Law and Shaun K. Roache1

Authorized for distribution by Matthew Jones

June 2015

Abstract

Assessing default risks for Chinese firms is hard. Standard measures of risk using market

indicators may be unreliable because of implicit guarantees, the large role played by less-

informed investors, and other market imperfections. We test this assertion by estimating

stand-alone 1-year default probabilities for non-financial firms in China using an equity-

based structural model and debt costs. We find evidence that the equity measure of default

risk is sensitive to a firm’s balance sheet health, profitability, and ownership; specifically,

default probabilities are higher for weaker, less profitable, and state-owned firms. In

contrast, measures based on the cost of debt seem largely detached from fundamentals and

instead determined by implicit guarantees. We conclude that for individual firms, equity-

based measures, while far from perfect, provide a better measure of stand-alone default

risks than borrowing costs.

JEL Classification Numbers: G12, G13, G17, G33

Author’s E-Mail Address: tlaw@imf.org; sroache@imf.org

1 We would like to thank Dale Gray, Matthew Jones, Hui Miao, Dragon Tang, and seminar participants at the

Hong Kong Institute for Monetary Research Annual China Conference for their comments. The usual

disclaimer applies.

IMF Working Papers describe research in progress by the author(s) and are published to

elicit comments and to encourage debate. The views expressed in IMF Working Papers are

those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board,

or IMF management.

2

CONTENTS PAGE

ABSTRACT ..............................................................................................................................2

I. INTRODUCTION ................................................................................................................3

II. MARKET-BASED DEFAULT PROBABILITIES FOR CHINESE FIRMS ...............4

III. CASE STUDIES ..............................................................................................................12

IV. AGGREGATE RESULTS ..............................................................................................14

V. DETERMINANTS OF DEFAULT PROBABILITIES IN CHINA .............................16

VI. CONCLUSION ................................................................................................................29

REFERENCES .......................................................................................................................30

TABLES

Table 1. Sample Summary Statistics: Balance Sheet Items .......................................................9 Table 2. Jump-Diffusion Structural Credit Model: Distribution of Estimated PDs ................11 Table 3. 1-Year Default Probability to Implied Credit Rating Mapping .................................11 Table 4. Firm-Specific Explanatory Variables ........................................................................18 Table 5. Macroeconomic Explanatory Variable ......................................................................19 Table 6. Default Probabilities Pooled Regression, Q1-2006 to Q3-2014 ................................21 Table 7. Comparison of Coefficients with Altman’s Model for U.S. Firms ...........................22 Table 8. Wald Test Results of Coefficients of State Ownership Variables .............................23

Table 9. Default Probabilities Pooled Regression, Q1-2006 to Q3-2014 ................................24 Table 10. Default Probabilities Pooled Regression, Q1-2006 to Q3-2014 ..............................26 Table 11. Default Probabilities Pooled Regression, Q1-2006 to Q4-2014 ..............................28

FIGURES

Figure 1. Skewness of Stock Returns in Chinese Equity Market ..............................................7 Figure 2. Chaori Solar Ltd, Nov-2011 to Mar-2014 ................................................................13 Figure 3. Vanke China Ltd, Jan-2006 to Mar-2014 .................................................................13

Figure 4. Default Probability-Implied Credit Rating

Distributions, Q4-2008, Q4-2013, Q4-2014 ............................................................................14 Figure 5. Default Probability-Implied Credit Rating

Distributions, Q4-2008, Q4-2013, Q4-2014 ............................................................................15

Figure 6. Leverage and Asset Volatility by Ownership Q4-2014 vs 2008 ..............................16

3

I. INTRODUCTION

Many policymakers, financial institutions, and investors have an urgent need to improve their

understanding of the credit risk of China’s corporate sector. As China continues to open up,

corporate credit exposures are growing quickly. A large suite of sophisticated methods to

quantify credit risk already exists but can it be applied to Chinese firms? China’s unique

financial system, including a prominent role for the state as a borrower and lender, could

mean that these tools will not work. A related complication is the absence of a full credit

cycle in China which can help to calibrate risks. Finally, China’s financial indicators—such

as equity prices and borrowing costs—may be less reliable gauges of credit risk than in other

countries. This could reflect implicit guarantees of state-owned enterprises (SOEs),

fragmented domestic markets with different investors, capital account restrictions, and a

smaller influence of global investors.

In this paper, we assess whether standard credit risk tools are useful for assessing Chinese

firms. We estimate the stand-alone 1-year probability of default for a sample of about 4,500

non-financial firms using a variant of Merton’s (1974) structural credit model. We allow for

unexpected jumps in default risk (Zhou, 1997), the inclusion of non-listed firms (Jobst and

Gray, 2013), and map to a database of actual defaults (Gray, 2009). Our definition of the

“stand-alone” probability of default is consistent with that of Standard & Poor’s (2010)

which is “an issuer's creditworthiness in the absence of extraordinary support or burden. It

incorporates direct support already committed and the influence of ongoing interactions with

the issuer's group and/or government.”

Our contribution is a comprehensive assessment of whether default probabilities calculated

using a structural model are related to firm fundamentals. Specifically, we follow Altman,

Fargher, and Kalotay (2011) (henceforth Altman et al.) and model the link between default

probabilities and firm fundamentals reported in their financial accounts, other firm-specific

characteristics such as ownership, and broad economic and financial conditions. We compare

the results from an equity model to one using borrowing costs to measure default probability.

This is a useful exercise for four reasons. First, it helps confirm whether firm-specific and

economic variables thought to influence default probabilities in developed market economies

are useful for inference in China. For example, are default probabilities higher for Chinese

firms with low profitability or weak balance sheets? We know that in developed markets the

answer is typically “yes”. Second, it can determine the extent to which firm ownership—

whether the firm is private or state-owned—influences default probabilities. Are default

probabilities lower for SOEs with implicit guarantees? Third, we can identify which

indicators—equity markets or borrowing costs—are more reliable for assessing stand-alone

credit risk. Finally, we can use this model to estimate default probabilities for firms that lack

market data (which includes a large proportion of China’s corporate universe) using the

firm’s accounts and other characteristics.

4

II. MARKET-BASED DEFAULT PROBABILITIES FOR CHINESE FIRMS

A. Methodology

We start from Merton’s (1974) structural model of credit risk as described by Gray and

Malone (2008) and Jobst and Gray (2013). Consider a firm for which the total market value

of its assets is denoted by V. This market valuation is derived from the expected present

value of the firm’s free cashflows discounted by the weighted average cost of capital as

shown by Damodoran (1996). The firm will default if its assets fall to a level—often defined

as a “default barrier” and denoted by DB—which produces cashflows that are insufficient to

service its debt. The specific value of DB in theory is the book value of the firm’s total

liabilities. In practice, DB is sometimes assumed to lie between total liabilities and current, or

short-term, liabilities to reflect that longer maturity debt need not be repaid immediately

(Crosby and Bohn, 2003).

Equity holders possess a junior contingent claim on the residual value of future assets. The

value of equity E at maturity can be considered as a call option on V with a strike price equal

to the DB:

(1)

Risky debt holders, in contrast, will either receive the book value of the firm’s liabilities DB

or, in the case of default, the firm’s remaining assets V. This payoff at maturity can be

described equivalently as the value DB minus a put option in which debt holders “sell” the

firm’s assets at a strike price DB:

(2)

(1) and (2) are the payoffs at maturity to European options with strike prices equal to the

default barrier DB. According to Jobst and Gray (2013), the risk-adjusted contingent claims

analysis (CCA) balance sheet then defines the value of the firm as V = D + E. Before using

these payoffs to calculate default probabilities, we follow Zhou (1997) and allow for the

possibility that changes in firm value are not normally distributed so that V follows a jump

diffusion process. The specific process for V under the risk-neutral measure is then:

(3)

In (3), is the expected rate of return, υ is the expected jump, σ is the instantaneous volatility

of V conditional on the jump not occurring, dZ is a Wiener process, and dY is a Poisson

process with intensity parameter λ. The jump size Π is a log-normally distributed random

variable:

(4)

The expected value of jump size can then be written as:

5

(5)

We estimate the parameters for each firm in our sample using a log-likelihood function and a

discrete probability density function. We solve for the jump diffusion process parameters (μ,

σ, λ, μπ, and σπ) based on the methodology developed in Ardia, David, Arango, and Gómez

(2011); specifically, if the intensity parameter λ is small then in a sufficiently short time

period then V will either jump once or not at all. This allows us to assume that the jump

probability ΔY during Δt is approximately equal to a Bernoulli random variable for small λΔt.

Denote the log change in the firm’s asset value by Δx, then the density of Δx during Δt is a

weighted mixture of densities given by:

λ . (6)

In (6), the Brownian motion part of the diffusion process is:

. (7)

And the jump part is:

. (8)

Given the empirical distribution of daily log changes in firm asset value, we maximize a log-

likelihood function over the set of parameter values θ = {μ, λ, σ, μπ, σπ}:

subject to the constraint

.

(9)

The value of λ is constrained to ensure that the probability that the asset value jumps once in

Δt is less than or equal to one (i.e., λΔt ≤ 1 where t = 1/252).

At a daily frequency, the market value of a firm’s assets V is not observable and this

necessitates the use of an iterative procedure to calibrate the jump diffusion parameters and

estimate firm value. Consider first the equation that calculates the price of a European call

option C on the firm’s assets V with a strike price equal to the default barrier DB:

where

(10)

6

.

In (10), T is the maturity of the option, r is the risk-free interest rate, and i is the number of

jumps over T. Given the equivalence between this call option and the value of equity from

(1), equity value can be seen as a function of asset value. If the jump diffusion parameters are

assumed to be known, we can solve for firm value V in each period with the Newton method.

In practice this method does not easily converge. An alternative approach to calculating the

call value is to use put-call parity where:

(11)

In (11), the call (equity) value is denoted by C, the put value is P, and the book value of

liabilities (default barrier) is DB. In this expression, e-rT

DB – P is the value of risky debt.

Following Jobst and Gray (2013), we set the default barrier DB equal to short-term liabilities

plus half of long-term liabilities. The put option premium can be calculated in the following

formula:

(12)

We can now solve for V using (10), (11) and (12) using jump diffusion parameters initially

estimated from the empirical distribution of equity returns. We update our parameter

estimates using the solution for V and then iterate this process until the estimates of V, μ, σ, λ, μπ, and σπ converged. We then follow Zhou (1997) and calculate the (risk-neutral and stand-

alone) default probability, denoting the drift μ by r for each individual firm using:

(13)

where ξ is the default barrier which we set to 1.

The incorporation of jump diffusion allows for a sudden change in firm value and default risk

which is a common feature in most equity markets, including China (Figure 1, panel 1). Jump

risk need not always be negative. Since 2010, returns for smaller firms have tended to be

positively skewed (sudden price increases) (Figure 1, panel 2). (13) can incorporate sudden

jumps in either direction and, as a result, we claim that it provides a better estimate for

default probability than a standard Merton model.

7

Figure 1. Skewness of Stock Returns in Chinese Equity Market 1. Skewness of daily returns for Shanghai Composite index and S&P 500 index (with 250-day rolling window)

2. Distribution of skewness of daily returns for listed firms in China (4-quarter rolling window)

Sources: Bloomberg and authors’ calculations.

Adjusting to real-world probabilities

Up to this point, we have been working with risk-neutral default probabilities which use the

risk-free rate of return as the Brownian motion drift rate in (13) and theoretical distributions

for firm values. Our aim is to uncover real-world (or actual) probabilities based on empirical

distributions because previous research has found that these can differ significantly from

risk-neutral estimates—see Chan-Lau (2006) and Sun, Munves, and Hamilton (2012). The

main reason is that risk-neutral probabilities overestimate the true probability of default

because they compensate investors for their unobservable aversion to bad outcomes.

Theoretical distributions, meanwhile, can overestimate default risk. This is because in reality,

distressed firms have strong incentives to adopt extreme measures to avoid default including,

for example, the sale of non-core assets. This can mean that the default probabilities of poor

credit quality firms can be overestimated.

We address these challenges by using two adjustments suggested by Gray (2009). First, to

better approximate Moody’s KMV EDFTM

s as described by Crosby and Bohn (2003), which

incorporate evidence from actual default histories, the asset volatility in (3) was calculated as

a positive linear function of the fitted asset volatility σv as written in (14a). Gray found that a

linear transformation of Moody’s published asset value volatility from (14a) in a structural

credit model produced default probabilities very close to KMV EDFTM

s. For Chinese firms,

we were unable to identify a clear relationship between our estimates of asset volatility and

those published by Moody’s. Therefore, to keep our process as transparent, we use our own

estimate of asset volatility as the independent variable σv in (14a) which is derived using (9)

and is available for all firms. In many cases, this produced default probabilities that share

similar features of EDFTM

s. Second, to convert risk-neutral to actual default probabilities, the

risk free-rate r in (13) was replaced by a drift term that is designed to capture the time-

8

varying price of risk and is calculated as the product of the correlation between the equity

price of the firm and the market and the Sharpe ratio. These adjustments, including the

parameter values suggested by Gray and KMV, are shown in (14):

(14a)

(14b)

Where: γ0 = 0.05; γ1 = 1.37; ρA,M = 0.6; and SR = 0.75.

In (14a), σV* denotes the linear transformation of the asset volatility σV that is estimated in

(9). In (14b), μ* denotes the expected return, r is the risk-free rate, ρA,M is the correlation, and

SR is the Sharpe ratio.

Two remarks are worth making related to the application of this method for China. First, the

linear transformation of the estimated asset volatility in (14a) effectively means that we are

fitting default probabilities on an approximation of Moody’s proprietary database of actual

default rates. This database includes only North American firms which operate in a very

different economic and legal environment to Chinese firms. Bankruptcy procedures in the

United States and Canada are well defined, tested through the economic cycle, and rarely

influenced by actual or prospective public sector bail-outs. These conditions do not yet hold

for China. For example, the 2014 World Bank’s “Doing Business” survey ranked China 53rd

in resolving insolvency (the United States and Canada ranked 4th

and 6th

, respectively),

mainly due to high costs, a low recovery rate, and a low probability that the firm would

emerge as a going concern. At the same time, China’s actual default rates may be suppressed

by public sector support, mainly for SOEs. We believe this adjustment still has merit,

however, as it provides an estimate of stand-alone default probabilities based on the

fundamental health of the firm rather than the intricacies of the legal system or complex

political economy. If a firm is unable to pay its obligations the financial costs must be borne

somewhere, and if not by bondholders then by banks or the public sector.

Second, we have to estimate the market price of risk and the Sharpe ratio. A common

approach is to estimate these two variables ex-post using historical data but this is not easy in

China mainly because ex-post Sharpe ratios have been close to zero or negative during 2008

to 2013, contrary to theoretical predictions (results not shown). One interpretation of this

outcome is that Chinese investors have been persistently surprised by low equity market

returns—in other words, they suffer from biased expectations. Of course, the risk price and

Sharpe ratio in the model should correspond to investors’ forward-looking rational

expectations rather the past so we use the theoretically-consistent prior in our estimates in

(14b).

B. Data

Sample of firms

Default probabilities were estimated for an unbalanced panel with a maximum of 4,483 non-

9

financial firms for the period between Q1-2006 and Q4-2014. Of this panel, 2,441 were firms

with listed equity on a public exchange and 2,042 firms were unlisted but had issued bonds in

the onshore bond market. Our sample includes 1,020 local government financing vehicles

(LGFVs) of which are listed firms. The LGFVs are identified when they issue urban

investment (Changtou) bonds in the onshore bond markets. Over this period, there have been

very few de-listings and any survivor bias in the sample is likely to be minimal.

Firms’ balance sheet items

All balance sheet data, including total assets, total liabilities, and current and non-current

liabilities were extracted from the WIND database. Listed firms report these variables shortly

following the end of each calendar quarter. Non-listed firms that issue bonds are required to

disclose their financial statements for the three years prior to issuance and every subsequent

year, although a few firms do report quarterly. As the data are available only for the period

up to their issuance, most of the firms do not have a complete time series during the sample

period. The main items among current liabilities are short-term loans, notes payable,

financial liabilities held for trading, accrued expenses, account payable, tax payable and

interest payable. Non-current liabilities include long-term loans, bonds payable, long-term

accounts payable, and deferred income tax liabilities. We included all types of liabilities in

our definition of the default barrier because of their material size and their status as

contingent claims on the firm. Summary statistics for selected balance sheet variables over

the sample period are provided in Table 1. Non-listed firms tend to be larger on average even

though an increasing number of smaller companies has issued bonds since 2008 (reducing

asset size for the median firm). The total liabilities of sample firms was about RMB53 trillion

as of Q4-2014, which accounted for about 45 percent of non-equity total social financing

(TSF).

Table 1. Sample Summary Statistics: Balance Sheet Items (Billions of yuan unless otherwise specified)

Q4 2014 1/ Q3 2008 1/

Median Std. Dev. Median Std. Dev.

Listed non-financial firms

Total assets 2.83 68.97 2.05 40.35

Total liabilities 1.16 39.82 1.06 18.27

Current liabilities 0.92 28.03 0.83 12.82

Non-current liabilities 0.11 13.57 0.09 5.98

Market cap 3.85 32.93 1.86 70.33

Number of firms 2,411 1,390

Non-listed non-financial firms

Total assets 7.55 185.07 9.32 120.48

Total liabilities 4.34 111.56 5.00 56.68

Current liabilities 2.37 47.35 3.17 30.90

Non-current liabilities 1.07 75.68 1.53 29.60

Number of firms 1,586 675

Source: WIND database and authors’ calculations.

1/ End-2013 and End-2008 for non-listed firms.

10

Estimating the market value of assets

Estimates for unobservable asset values and asset volatilities for each listed firm were based on

the quarter-end levels and rolling 250-day standard deviations of the log changes in equity

market capitalizations, respectively, with data sourced from Bloomberg. For dual-listed shares,

the market capitalization is calculated as the sum of all listings for each firm converted into

yuan at the prevailing exchange rate, including H shares traded in Hong Kong SAR.

To extend our analysis to non-listed firms, we followed Jobst and Gray (2013) who used peer

group matching in their contingent claims analysis of two non-listed banks in the United

Kingdom (IMF, 2011). As a first step, we grouped non-listed firms according to the sub-industry

classification provided in the WIND database (the most detailed level available). An alternative

was the classification adopted by the China Securities Regulatory Commission (CSRC) but this

differed between listed and non-listed firms. In the second step, we minimize the squared

distance (DS) for each non-listed firm i and all the listed firms j = 1…N in the same sub-industry

over a vector of dispersion-standardized characteristic variables including size (the book value of

assets or BV) and leverage (debt-to-equity or D/E) over the sample period:

(15)

The procedure (15) thus equally weights relative firm size and leverage when minimizing

distance. We then solve for the non-listed firms’ market values of assets and jump diffusion

parameters using the same process described above but using the book value of equity multiplied

by the median price-to-book ratio of the same sector as listed firms.

C. Default Probability Under-Prediction

It is well known that structural credit models tend to under-predict default probabilities,

particularly at short horizons of about one year and for investment grade debt (Leland, 2006).

Notwithstanding our incorporation of jump-risk into the basic model of section II and the

linear transformation (14a) to better reflect actual default rates, we still arrive at default

probabilities that appear unreasonably low. This statement assumes that China’s actual

default rates would have been materially above zero in the absence of third-party financial

support that appears to have suppressed the frequency of credit events, at least as measured

by the bond market. As Table 2 shows, the default probability of the upper quartile firm (i.e.,

the firm with a default probability at the 75th

percentile of the full sample) at the end of Q1

2014 was just 0.1 percent. Alternatively, mapping the default probabilities of the sample into

credit ratings suggests that 89 percent of firms were investment grade at the end of Q1-2014.

11

Table 2. Jump-Diffusion Structural Credit Model:

Distribution of Estimated DPs (Q1-2014)

Default Probability Cumulative Number of Firms

10th percentile 1.7X10-20

400

25th percentile 4.3X10-9

1,000

50th percentile 0.0006 1,999

75th percentile 0.1 2,998

90th percentile 0.6 3,597

Max 46.9 3,997 Source: Authors’ calculations.

This is unlikely to just be a China-specific issue as it is a finding in studies of credit risk in

advanced economies (Huang and Huang, 2012). Under-prediction may reflect, in part,

technical shortcomings of the jump diffusion calibration, including its limited ability to

capture volatility clustering (Kou, 2008).

D. Default Probability-Implied Credit Ratings

We follow Hui, Wong, Lo and Huang (2005) and complement the reporting of estimated

default probabilities with implied credit ratings. Hui et al. (2005) found a close fit between a

least squares fit of credit model-generated default probability term structures and published

ratings. Our mapping does not correct for downward bias in the same way as Hui et al and is

designed only to provide an alternative means of reporting. We assign an implied credit

rating by mapping the estimated 1-year default probability to the actual default rates by

published credit rating as provided by Standard & Poor’s (2014). The range of default

probabilities for each credit rating is determined by the mid-point of the averages of two

adjacent ratings over the interval (0,1) (Table 3).

Table 3. 1-Year Default Probability to Implied Credit Rating Mapping

Implied Credit Rating Issuer-Weighted

Long-Term Average Lower Limit Upper Limit

AAA 0.00 0.00 0.01

AA 0.02 0.01 0.05

A 0.07 0.05 0.14

BBB 0.21 0.14 0.51

BB 0.80 0.51 2.46

B 4.11 2.46 15.49

CCC/C 26.87 15.49 100.00

Source: S&P (2014) and authors’ calculations.

12

III. CASE STUDIES

Before discussing some of the results at the aggregate level, we will present in this section

some case studies to give a sense of how the model performs for specific firms.

A. Shanghai Chaori Solar Energy Science and Technology Ltd

Our first case is the first (and as yet only) default in China’s domestic bond market. Shanghai

Chaori Solar Energy Science and Technology Ltd (Chaori) manufactures solar energy

products for both export and domestic residential installation. Immediately after the Global

Financial Crisis (GFC), which was preceded by a spike in oil prices, solar was seen as a

growth industry that could potentially benefit from government support. (For example, the

U.S. solar power market grew by 67 percent in 2010 to record the fastest growth of any

energy sector.) This helped privately-owned Chaori to raise 2.38 billion yuan in its

November 2010 IPO. Supply outpaced demand, however, and the solar industry has been

plagued by overcapacity problems for some time. It was likely no surprise that many of its

firms started to see profitability deteriorate as the post-GFC surge in economic activity

moderated. Chaori started reporting large and persistent losses and sharply increasing

leverage in late 2012.

The model first identified Chaori’s rising default risk in July 2013 when the 1-year default

probability (DP) picked up from near zero to over 10 percent as the stock price began to fall.

Thereafter, the DP dipped to the 3-5 percent range before spiking up to over 25 percent by

the end of 2013. The corresponding changes in implied credit ratings would be from AAA-

AA to B and finally to CCC. As Figure 2 shows, a declining distance-to-default explains a

large part of the rise in DPs, as our estimate of the market value of assets declined through

2013. At the same time, the expected volatility of asset values and “jump risk”—seen by a

downward skew in the estimated distribution of asset values—both increased. In March

2014, Chaori was unable to meet a coupon payment on the 5-year 1 billion yuan bond it had

issued in May 2012.

B. Vanke China Ltd

Vanke China Ltd is one of the largest property developers in the country. The privately-

owned firm was founded in 1984, commenced real estate activities in 1988, and became the

second listed company on the Shenzhen Stock Exchange in 1991. Vanke specializes in

residential sales and enjoyed rapid growth in turnover as China’s property market boomed

through late 2013. While still regarded as one of the strongest firms in the sector—as

evidenced by investment grade credit ratings by the three largest global agencies—Vanke’s

leverage has increased and its debt servicing capacity has eroded over recent years. The firm

is clearly exposed to the property market cycle.

The model shows that the estimated 1-year DP increased significantly during 2008 as the

market value of assets declined and asset volatility increased. Following the recovery of 2009

and through early 2013, the DP has stayed low as rising asset values and low volatility offset

a large rise in the firm’s liabilities. As the property market has slowed since early 2013, a

gradual decline in asset values has combined with rising asset volatility to lift the DP to

13

between 2-4 percent.

Figure 2. Chaori Solar Ltd, Nov-2011 to Mar-2014 1. Firm Value and the Default Barrier (billions of yuan)

2. Estimated 1-Year Default Probability (decimals)

Sources: Wind; Bloomberg; and authors’ calculations.

Figure 3. Vanke China Ltd, Jan-2006 to Mar-2014 1. Firm Value and the Default Barrier (billions of yuan)

2. Estimated Probability of Default

Sources: Wind; Bloomberg; and authors’ calculations.

0

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Default barrier

Estimated volatility bands

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14

IV. AGGREGATE RESULTS

In this section, we present an aggregated summary of the results from the model described in

section I and focus on how default probabilities for a large sample of Chinese firms has

changed between Q1-2006 and Q4-2014. For each quarter, we use the methodology

described by (1)-(14) to estimate 1-year default probabilities for all firms and then describe

the resulting distribution for the full sample and along different dimensions, including

sectors, listing status, and ownership structure.

The distribution of default probabilities shifted significantly higher during the GFC but has

remained quite stable and low since 2012. Notwithstanding increased liabilities across most

firms, rising asset values and remarkably low equity price volatilities since 2012 have helped

keep default probabilities low.

Figure 4 panel 1 shows the distribution of mapped credit ratings for selected sectors. We

show two segments of the distribution where credit default risks are higher than for the

median firm. The greater the degree of concentration of firms with weak ratings, the further

to the left of the scale each segment will start. For example, from the median to the 75th

percentile firm, implied credit ratings for construction, real estate, retail and wholesale, and

utilities compares unfavorably to the rest of the sample. For these sectors, this segment of the

distribution is mostly sub-investment grade. Figure 4 panel 2 compares the 90th

percentile

firms in each sector at the end of Q4-2014, Q4-2013, and the quarter which saw default risks

reach their maximum for most firms, Q4-2008. This provides one measure of the “weak tail”

of the distribution. Rising equity market valuations have lowered default risks over the last

year but construction, real estate, and retail and wholesale continue to stand out as most

vulnerable.

Figure 4. Default Probability-Implied Credit Rating Distributions, Q4-2008, Q4-2013, Q4-2014

1. Distributions by Sector, Q4-2014 2. 90th Percentile Firms by Sector

Sources: Wind; Bloomberg; and authors’ calculations.

-1 2 3 4 5

Construction

Real Estate

Wholesale and Retail

Utilities

Mining

Others

1-50 51-75 75-90

B AAAAAABBBBB -1 2 3 4 5 6 7

Construction

Real Estate

Wholesale and Retail

Utilities

Mining

Others

2014Q4 2013Q4 2008Q4

B AAAAABBBBBCCC A

15

At the end of Q4-2014, the distribution of default risk is similar for listed and non-listed

firms (Figure 5). Our main finding is that unconditional stand-alone default risk appears to be

much higher for SOEs—particularly locally-owned SOEs—compared to LGFVs and

privately-owned firms. For example, the range of implied credit ratings for local SOE firms

between the 75th

and 90th

percentile are close to A-BB. In contrast, the range for LGFVs is

almost all investment grade from AAA to BB.

Figure 5. Default Probability-Implied Credit Rating

Distributions, Q4-2008, Q4-2013, Q4-2014 1. Listing Status and Ownership, Q4-2014 2. 90th Percentile Firms by Listing/

Ownership

Sources: Wind; Bloomberg; and authors’ calculations.

During Q4-2014, default probabilities were depressed by lower asset volatilities (Figure 6,

panel 1). The weak tails the distribution of both central and local SOEs have lower

volatilities than other privately-owned firms, offsetting generally higher leverage among

these firms (Figure 6, panel 2). The leverage of local SOEs and LGFVs has continued to rise

while that of other firms has been more stable. Lower default probabilities for LGFVs may

be attributable to lower asset volatility and leverage.

-1 2 3 4

Central SOE

Local SOE

LGFV

Others

Listed

Non-listed

1-50 51-75 75-90

B BB BBB A AA AAA -1 2 3 4 5 6 7

Central SOE

Local SOE

LGFV

Others

Listed

Non-listed

2014Q4 2013Q4 2008Q4

CCC B BB BBB A AA AAA

16

Figure 6. Leverage and Asset Volatility by Ownership Q4-2014 vs 2008

1. Asset Volatility by Ownership (percent)

2. Debt-to-Equity Ratio by Ownership

Sources: Wind; Bloomberg; and authors’ calculations.

V. DETERMINANTS OF DEFAULT PROBABILITIES IN CHINA

In this section, we establish an empirical link between market-based assessments of default

probabilities and a set of potential explanatory variables including firm-specific accounting

ratios and economic and financial conditions. Our aim is to understand whether market-based

measures of default probability are affected by factors that, intuitively, should influence

credit risk. In other words, are we justified in using standard methods of credit risk

assessment in China or is China different?

0%

10%

20%

30%

40%

50%

60%

70%

80%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Central SOE

Local SOE

LGFV Others Central SOE

Local SOE

LGFV Others

90th percentile75th percentile50th percentile25th percentile10th percentile

2014Q4 2008Q4

0

1

2

3

4

5

0

1

2

3

4

5

Central SOE

Local SOE

LGFV Others Central SOE

Local SOE

LGFV Others

90th percentile75th percentile50th percentile25th percentile10th percentile

2014Q4 2008Q4

17

A. Previous Literature

To allow for comparisons with earlier literature, we follow Altman et al. (2011) who

quantified these linkages using a logit model for a large sample of firms in the United States

over a sample period covering 1978 to 2007. The dependent variable in their model is the

risk-neutral default probability estimated from a structural credit model using equity market

capitalizations, equity price volatilities, and a distress barrier of short-term liabilities plus

one-half of long-term liabilities. Their explanatory variables are largely taken from Altman’s

(1968) seminal Z-score paper and include one-quarter lagged firm-specific indicators of

profitability, leverage and liquidity. Firm size and age are also included. They find, on the

basis of pooled regressions, that these accounting-based measures explain about 40 percent

of the total in-sample variation in market-based default probabilities. All of the Z-score

variables are correctly signed and statistically significant. Zhang, Han, and Chan (2014)

adopt a similar approach for China and find that a set of Altman Z-score variables accounted

for about half of the estimated variation in default probabilities.

Much of the previous research linking default probabilities to fundamental factors use actual

default rates with the dependent variable taking a value of 1 in the event of “default” and

zero otherwise. This is not a useful approach for China given the paucity of data related to

confirmed credit events but this literature can provide some perspective for the results that

we present in this paper. In almost all cases, the choice of firm-specific explanatory variables

includes various measures of profitability, interest coverage, leverage, and liquidity. Growth

and firm size and age are often also included. Jacobson, Lindé, and Rozbach (2013) exploit a

large dataset on the payment behavior of Swedish firms between 1990-2009 and classify a

firm as having default status conditional on the occurrence of any one of five events,

including declaration of bankruptcy or suspension of payments (including debt service or

other obligations). Macroeconomic variables include an estimate of the output gap, annual

inflation, the nominal policy interest rate, and the de-trended real effective exchange rate,

They find that firm-specific variables are important determinants of relative default

likelihood but macroeconomic variables exert much greater influence on average economy-

wide default risk, as might be expected. Bonfim (2009) considered a large sample of

Portuguese firms using annual data over1996-2002 and estimated a random-effects probit

model in which the firm defaults if it becomes overdue on a bank loan payment. She finds

that firm-specific explanatory variables (lagged by one year) are important determinants of

default probability. She also finds that economic sectors and macroeconomic variables such

as GDP growth, interest rates, and loan growth contributed to default risk in terms of

systematic shocks but tend to exert less influence than firm-specific factors. Benito, Delgado,

and Pagés (2004) reached a similar conclusion for Spanish firms.

B. Methodology and Data

Specification and estimation

We use a standard logit pooled regression specification which can be written as:

18

Where

And from (13)

(16)

In (16) for each time t and firm i, α is a common intercept, β is a (k x 1) vector of parameters,

Xit-1 is a (k x 1) vector of k variables specific to firm i and lagged one period, γ is an (l x 1)

vector of parameters, Zt-1 is an (l x 1) vector of macroeconomic variables lagged one period,

ζ is an (m x 1) vector of parameters, D is an (m x 1) vector of dummy variables, and λt is a

time effect. The residual terms εit are assumed to be independent across firms after

controlling for common factors. The firm specific variables were winsorized at the 1st and

99th

percentiles to remove the effect of extreme outliers. We estimated (16) using OLS with

robust standard errors. Our sample includes an unbalanced panel of 2,409 listed firms for

which firm-specific equity market data were available.

Firm-specific explanatory variables

Our choice of firm-specific explanatory variables denoted by Xit-1 in (16) directly follows

Altman et al. (2011) and is shown in Table 4. The first four variables are taken from the well-

known Z-score model of default risk described in Altman (1968) and are augmented with

indicators of size and age. Table 6 also shows our expectations for the signs on the

coefficients included in the parameter vector β in (16). We would expect that default

probability should be decreasing with profitability, liquidity, and firm age and increasing

with leverage and the proportion of short-term liabilities. The summary statistics are

calculated after winsorization at the 1st and 99

th percentiles, respectively. On the basis of

standard panel unit root tests (results not shown), we found strong evidence that these series

are stationary.

Table 4. Firm-Specific Explanatory Variables

Description Variable Median Standard Deviation

Expected Sign

Profitability: ratio of earnings before interest and taxes to total assets

-0.06 0.08 –

Profitability: ratio of retained earnings to total assets

-0.14 0.27 –

Leverage: ratio of total assets to total liabilities

1.73 0.66 +

19

Table 4. Firm-Specific Explanatory Variables

Description Variable Median Standard Deviation

Expected Sign

Liquidity: ratio of working capital to total assets

-0.16 0.31 –

Debt maturity structure: ratio of current liabilities to non-current liabilities

2.30 30.36 –

Relative size: ratio of total assets to median of total assets of all sample firms

0.57 8.87 +/–

Age: number of months since listing

112.37 68.54 +

Economic and financial conditions explanatory variables

Our choice of macroeconomic explanatory variables denoted by Zt-1 in (16) includes a survey

of manufacturing activity, broad money growth, and the real lending rate (Table 5). As the

weighted average lending rate is available only from 2008Q4, the 1-year average lending rate

is used during Q1-2006 to Q3-2008 (discontinued from Q4-2008).

Table 5. Macroeconomic Explanatory Variable

Description

Variable Expected Sign

Economic outlook: Official manufacturing PMI

+

Monetary conditions: M2 year-on-year growth deflated by CPI inflation

+

Monetary conditions: Average lending rate deflated by CPI inflation

–

Market valuation: Price-to-book ratio of Shanghai Composite index

+

Industry, period-effect, and ownership dummy variables

We follow Bonfim (2009) and include quarterly (time-effect) and sector dummies as denoted

20

by D in (16), the latter using the China Securities Regulatory Commission (CSRC) industry

classification. We also include a set of dummies denoting whether the firm is centrally state

owned, locally state owned, an LGFV, or privately owned. Previous research suggests that

ownership may be important for stand-alone default risk—for example, Zhang et al (2014)

argue that a state ownership stake in a firm of 50 percent or more is associated with higher

default likelihood. Our underlying assumption is that the payoffs from the equity of Chinese

firms are not implicitly guaranteed. This should mean that an equity market-based estimate of

default risk reflects more the impact of state ownership on the probability that the firm is

unable to service its obligations from its own resources rather than the probability of public

sector financial support.

C. Results

The results from a range of specifications based on (16) are shown in Table 6.

Firm-specific explanatory variables

The effect of the firm-specific variables denoted by β in (16) is largely in line with our

expectations and similar to, or somewhat larger than, those from the study of U.S. firms by

Altman et al. (2011). This is shown in Table 7 for the most basic specification (1) which

excludes industry and seasonal dummies to allow a like-for-like comparison. The estimated

coefficients on both profitability indicators, ln(1-EBIT/TA) and ln(1-RE/TA), were correctly

signed (negative), statistically significant, and robust across specifications. In other words,

rising profitability lowers default risk, all else equal.

The effect of retained earnings, an indicator of past profitability, was much higher in our

model than Altman et al. (2011). This may reflect our inclusion of an additional coefficient to

account for a peculiarity in China—retained earnings were negative for about one-fifth of

sample observations. As Table 6 shows, the median level and standard deviation of this

variable is not large and its total effect is, on average, small.

The estimated coefficient on firm leverage, 1+ln(TA/TL), is correctly signed (positive),

statistically significant, and robust across specifications. For the hypothetical A-rated firm, In

common with Altman et al. (2011), we find that the estimated coefficients on indicators of

liquidity, debt structure, size, and age are either statistically insignificant, economically

insignificant, or not robust to different specifications.

To assess the economic significance of these coefficients, we calculate the estimated

marginal impact of these firm-specific variables on default probabilities. For example, for an

A-rated firm with a 1-year estimated default probability of 0.07 percent, a fall in the asset-to-

liabilities ratio from 5 to 4 (consistent with 25 percent increase in liabilities) would increase

the default probability to 0.19 percent. For the same firm, a decline in profitability measured

as a 10ppts fall in the EBIT-assets ratio would increase the default probability to 0.09

percent.

21

Table 6. Default Probabilities Pooled Regression, Q1-2006 to Q3-2014

Source: Authors’ estimates. 1/ ** represents statistically significance at 5 percent level. T-statistics in parentheses.

1 2 3 4 5 6 7 8 9

Constant-4.46**

(-14.55)

-1.62**

(-3.71)

-4.98**

(-9.75)

-70.48**

(-15.21)

-128.59**

(-27.74)

-1.73**

(-3.97)

-5.02**

(-9.84)

-70.17**

(-15.15)

-128.70**

(-27.77)

ln(1-EBIT/TA)-3.43**

(-3.50)

-3.71**

(-3.77)

-9.91**

(-9.90)

-3.89**

(-3.91)

-5.30**

(-5.34)

-3.70**

(-3.76)

-9.88**

(-9.88)

-3.92**

(-3.94)

-5.30**

(-5.35)

WC/TA0.75**

(2.55)

0.89**

(2.59)

-1.05**

(-3.07)

1.05**

(3.02)

-0.12

(-0.35)

1.10**

(3.19)

-0.88**

(-2.60)

1.24**

(3.60)

0.06

(0.17)

ln(1-RE/TA)-8.78**

(-11.72)

-8.37**

(-11.11)

-2.90**

(-3.82)

-8.47**

(-11.16)

-6.42**

(-8.49)

-8.57**

(-11.39)

-2.94**

(-3.87)

-8.64**

(-11.40)

-6.53**

(-8.64)

Negative DV X

ln(1-RE/TA)

19.47**

(22.36)

18.23**

(20.70)

12.56**

(14.33)

18.45**

(20.86)

16.16**

(18.34)

18.54**

(21.13)

12.66**

(14.46)

18.73**

(21.23)

16.35**

(18.60)

1+ln(TA/TL)9.77**

(62.53)

9.33**

(55.15)

9.54**

(58.46)

9.30**

(55.00)

9.41**

(56.33)

9.29**

(54.90)

9.49**

(58.19)

9.26**

(54.74)

9.37**

(56.07)

Size-0.02**

(-6.48)

-0.01**

(-4.19)

-0.03**

(-7.55)

-0.01**

(-3.35)

-0.02**

(-5.29)

-0.01

(-1.89)

-0.02**

(-5.83)

0.00

(-1.08)

-0.01**

(-3.27)

ln(CL/NCL) -0.0086**

(-3.0500)

-0.0105**

(-3.7000)

-0.0022

(-0.8100)

-0.0086**

(-3.0400)

-0.0073**

(-2.6100)

-0.0099**

(-3.5000)

-0.0018

(-0.6500)

-0.0080**

(-2.8200)

-0.0067**

(-2.4200)

Age 0.0037**

(3.6700)

0.0079**

(7.4500)

-0.0020

(-1.8600)

0.0083**

(7.7400)

0.0041**

(3.8100)

0.0066**

(6.3000)

-0.0027**

(-2.5300)

0.0069**

(6.6000)

0.0030**

(2.8900)

LGFV dummy

-2.94**

(-7.88)

-2.16**

(-5.88)

-2.92**

(-7.78)

-2.58**

(-6.96)

Local SOE

dummy

-1.87**

(-13.33)

-1.10**

(-8.07)

-1.85**

(-13.20)

-1.53**

(-11.03)

Central SOE

dummy

-1.23**

(-6.87)

-0.37**

(-2.14)

-1.22**

(-6.82)

-0.85**

(-4.82)

Estimated SOE

shareholding

-3.42**

(-12.38)

-1.99**

(-7.42)

-3.39**

(-12.31)

-2.82**

(-10.33)

ln(PMI)17.11**

(14.80)

33.80**

(28.77)

17.01**

(14.71)

33.82**

(28.80)

Real money

supply growth

-11.01**

(-7.82)

7.23**

(4.83)

-11.31**

(-8.03)

7.12**

(4.75)

Real lending

rate

67.36**

(12.24)

-36.16**

(-5.70)

68.63**

(12.47)

-35.77**

(-5.63)

PBV ratio-2.63**

(-38.12)

-2.64**

(-38.35)

Dummies

Industry N Y Y Y Y Y Y Y Y

Seasonal N Y Y Y Y Y Y Y Y

Quarterly N N Y N N N Y N N

No. of obs. 58,258 58,258 58,258 58,258 58,258 58,258 58,258 58,258 58,258

R-squared 0.168 0.174 0.225 0.177 0.193 0.173 0.225 0.176 0.192

Dependent variable: logit function of probability of default (specifications 1-9)

22

Table 7. Comparison of Coefficients with Altman’s Model for U.S. Firms

Explanatory Variables Specification 1

Coefficients Altman et al. (2011)

Coefficients

-3.43** (-3.50)

-4.25** (-55.9)

0.75** (2.55)

0.78** (49.8)

-8.78**

(-11.72) -0.81** (-129)

9.77**

(56.66) 2.11** (304)

-0.02

(-6.48) 0.58** (299)

-0.0086** (-0.6900)

-0.13** (-62.6)

0.0037**

(3.67) 0.0015**

(74.8)

Note: Newey and West (1987) corrected t-statistics are in parentheses.

Ownership

State ownership appears to increase the probability of default, all else equal. It is worth

reiterating that we are measuring the stand-alone probability, excluding the likelihood of

state financial support, from the perspective of an equity investor. The estimated coefficients

on dummy variables denoting whether the firm is a centrally-owned or locally-owned SOE or

an LGFV were negative (positive relation with default probability), statistically significant,

and robust to different specifications. Some caution should be used when considering the

coefficient for LGFVs as most of these firms are non-listed and not included in the regression

sample.

To provide some quantitative context, again consider an A-rated privately-owned firm with a

default probability of 0.07 percent. Using the results from specification 5 in Table 6, for the

same set of firm-specific and macroeconomic variables, the default probability of a central

SOE would be 0.06ppt higher, which would be equivalent to a rating one notch lower at A-

minus. For a local SOE, the default probability would rise by 0.1ppt, which would imply a

rating two notches lower at BBB. Finally, for an LGFV the default probability would rise by

0.17ppt or an implied rating close to BBB-minus.

The estimated coefficient on a variable that measures the proportional stake held by the

public sector was also negative and statistically significant. Using the results from

specification 5, the estimated coefficient implies that for an A-rated firm, the default

probability would rise by 0.02ppt for every 10ppt increase in public ownership. We were able

23

to reject the null hypothesis that ownership variables have no impact on default probability at

the usual levels of confidence (Table 8).

Table 8. Wald Test Results of Coefficients of State Ownership Variables

Specification with All Macro Variables Specification with All Macro Variables

Constraints on

parameter vector ζ Chi-squared Constraints Chi-squared

(1) ζCentral SOE = 0 23.20** (1) =0 106.79**

(2) ζLocal SOE = 0 121.59**

(3) ζLGFV = 0 48.44**

Wald test statistic 46.86** Wald test statistic 106.79**

** represents statistically significant at 5 percent level.

Separately, we estimated the model for the sample of state-owned enterprises (SOEs) and

local government financing vehicles (LGFVs) (Table 9). While coefficients on firm specific

variables remained significant with expected signs, the results reiterated that central SOEs

have lower default probabilities than those for local SOEs, which were lower than LGFVs.

The differences of ratings are similar to specification 5 in Table 8.

24

Table 9. Default Probabilities Pooled Regression for state-owned enterprises and local government financing vehicles, Q1-2006 to Q3-2014

Source: Authors’ estimates. 1/ ** represents statistically significance at 5 percent level. T-statistics in parentheses.

1 2 3 4 5 6 7 8 9 10 11 12

Constant -6.74**

(-12.26)

-4.14**

(-5.94)

-4.09**

(-5.78)

-3.85**

(-4.63)

-52.95**

(-8.81)

-101.43**

(-16.67)

-4.63**

(-9.07)

-2.31**

(-3.66)

-2.26**

(-3.52)

-1.86**

(-2.39)

-51.02**

(-8.50)

-99.52**

(-16.40)

ln(1-EBIT/TA)-4.76**

(-3.57)

-4.74**

(-3.51)

-4.76**

(-3.52)

-10.01**

(-7.25)

-4.76**

(-3.48)

-5.98**

(-4.36)

-4.38**

(-3.30)

-4.50**

(-3.34)

-4.51**

(-3.35)

-9.81**

(-7.13)

-4.52**

(-3.31)

-5.76**

(-4.22)

WC/TA-1.61**

(-4.18)

-0.60

(-1.30)

-0.62

(-1.32)

-2.27**

(-4.93)

-0.38

(-0.81)

-1.35**

(-2.91)

-1.24**

(-3.24)

-0.28

(-0.60)

-0.29

(-0.62)

-1.95**

(-4.26)

-0.05

(-0.11)

-1.03**

(-2.23)

ln(1-RE/TA)-10.61**

(-10.58)

-10.39**

(-10.27)

-10.40**

(-10.28)

-5.99**

(-5.87)

-11.07**

(-10.88)

-8.99**

(-8.85)

-10.63**

(-10.57)

-10.35**

(-10.22)

-10.37**

(-10.23)

-5.97**

(-5.85)

-11.02**

(-10.81)

-8.97**

(-8.83)

Negative DV X

ln(1-RE/TA)

21.34**

(17.17)

21.24**

(16.99)

21.25**

(16.99)

16.75**

(13.54)

21.99**

(17.54)

19.80**

(15.90)

21.46**

(17.25)

21.23**

(16.98)

21.24**

(16.98)

16.73**

(13.51)

21.97**

(17.52)

19.79**

(15.89)

1+ln(TA/TL)9.26**

(38.39)

8.74**

(33.77)

8.75**

(33.79)

9.33**

(37.28)

8.65**

(33.34)

9.01**

(35.24)

9.20**

(38.08)

8.68**

(33.49)

8.68**

(33.51)

9.26**

(37.00)

8.59**

(33.07)

8.95**

(34.97)

Size-0.01**

(-3.02)

-0.01**

(-2.52)

-0.01**

(-2.56)

-0.02**

(-5.42)

-0.01**

(-2.02)

-0.01**

(-3.61)

0.00

(-1.38)

0.00

(-0.94)

0.00

(-0.97)

-0.01**

(-3.82)

0.00

(-0.44)

-0.01**

(-1.98)

ln(CL/NCL) -0.0085

(-1.8000)

-0.0095**

(-1.9800)

-0.0094**

(-1.9700)

-0.0028

(-0.6100)

-0.0094

(-1.9500)

-0.0066

(-1.4000)

-0.0074

(-1.5600)

-0.0088

(-1.8300)

-0.0087

(-1.8100)

-0.0020

(-0.4300)

-0.0086

(-1.7900)

-0.0058

(-1.2300)

Age0.0089**

(6.3700)

0.0101**

(7.0700)

0.0100**

(7.0300)

-0.0018

(-1.1800)

0.0119**

(8.1800)

0.0058**

(3.9800)

0.0081**

(5.7800)

0.0095**

(6.5900)

0.0094**

(6.5500)

-0.0026

(-1.7400)

0.0112**

(7.6700)

0.0051**

(3.4300)

Local SOE

dummy

1.40**

(4.21)

1.20**

(3.19)

1.20**

(3.20)

1.19**

(3.26)

1.21**

(3.22)

1.19**

(3.20)

Central SOE

dummy

2.20**

(6.28)

1.92**

(4.98)

1.92**

(4.99)

1.91**

(5.07)

1.94**

(5.03)

1.91**

(4.99)

Estimated SOE

shareholding

-0.90

(-1.74)

-0.90

(-1.73)

-0.90

(-1.73)

-1.35**

(-2.67)

-0.87

(-1.67)

-1.18**

(-2.29)

ln(PMI)11.91**

(7.98)

25.97**

(16.90)

11.89**

(7.96)

25.98**

(16.91)

Real money

supply growth

3.08

(1.71)

17.80**

(9.35)

2.95

(1.63)

17.70**

(9.29)

Real lending

rate

26.62**

(3.78)

-62.78**

(-7.73)

27.20**

(3.86)

-62.41**

(-7.68)

PBV ratio-2.27**

(-25.22)

-2.27**

(-25.26)

Dummies

Industry N Y Y Y Y Y N Y Y Y Y Y

Seasonal N N Y Y Y Y N N Y Y Y Y

Quarterly N N N Y N N N N N Y N N

No. of obs. 29,435 29,435 29,435 29,435 29,435 29,435 29,435 29,435 29,435 29,435 29,435 29,435

R-squared 12.4% 12.8% 12.9% 17.8% 13.1% 14.8% 12.3% 12.8% 12.8% 17.7% 13.1% 14.7%

Dependent variable: logit function of probability of default (specifications 1-12)

25

Economic and financial conditions

The estimated coefficients on the economic and financial conditions variables—denoted by γ

in (16)—while correctly signed and statistically significant in some specifications—are much

less robust than the firm-specific fundamentals. The inclusion of the market price-to-book

ratio as an indicator of “market sentiment” had a large effect on the estimates for some of

these variables. Most robust was the finding that a rise in the PMI index of manufacturing

activity lowered the probability of default, all else equal. These results are consistent with

Bonfim (2009) and Jacobson et al. (2013). To put the estimates into context, consider an A-

rated firm with a 1-year default probability of 0.07 percent (Table 3). Using the results from

specification 5 in Table 8, for a two standard deviation (2pt) decline in the PMI, this firm’s

default probability would rise by about 0.09ppt, which pushes the rating two notches lower to

BBB. The impact of the aggregate financial variables is less robust and highly sensitive to

the inclusion of market sentiment indicators, such as the PBV ratio.

Overall model fit and robustness

In line with the previous literature, we find that firm-specific variables contribute more to in-

sample predictive power default probabilities than macroeconomic variables. Measures of

model fit, including adjusted R-squared, improve only marginally with macroeconomic

variables, notwithstanding the statistical significance of their marginal effects. The adjusted

R-squared of the estimations ranged from 16-23 percent and while this range is lower than

for the study of U.S. firms by Altman et al. (2011) it is not unusually low for this literature

and is close to the numbers reported by Benito et al. (2004) and Bonfim (2009).

We find that firm-specific variables still contribute more to in-sample predictive power of

default probabilities than macroeconomic variables even if the sample excludes central and

local state-owned enterprises and local government financing vehicles. The predictive power

of monetary conditions becomes less significant for a sample excluding SOEs and LGFVs

which may suggest that these firms are more sensitive to monetary policies, perhaps because

of their easier access to credit.

At the same time, it suggests that other important variables may be missing from the model,

including unobservable market sentiment (which we may have captured imperfectly using

the PBV ratio) and adverse shocks to the market value of assets, which are stemming from

the information asymmetry between investors. Estimates from panel regressions that include

fixed-effects (not shown) are quantitatively similar to the results in Table 6—the size of

significant firm-specific and macroeconomic variables are somewhat lower and higher,

respectively. The adjusted R-squared rises to about 33 percent.

26

Table 10. Default Probabilities Pooled Regression excluding state-owned firms, Q1-2006 to Q3-2014

Source: Authors’ estimates. 1/ ** represents statistically significance at 5 percent level. T-statistics in parentheses.

1 2 3 4 5 6

Constant -3.59**

(-8.42)

-2.28**

(-3.73)

-1.70**

(-2.70)

-1.81**

(-2.02)

-100.85**

(-14.22)

-168.88**

(-24.16)

ln(1-EBIT/TA)-3.82**

(-2.66)

-3.82**

(-2.66)

-3.92**

(-2.73)

-10.61**

(-7.29)

-4.77**

(-3.29)

-6.00**

(-4.16)

WC/TA1.88**

(4.12)

2.44**

(4.83)

2.39**

(4.73)

0.36

(0.71)

2.47**

(4.88)

1.14**

(2.24)

ln(1-RE/TA)-7.33**

(-6.43)

-6.98**

(-6.10)

-6.98**

(-6.11)

-0.49

(-0.43)

-6.54**

(-5.68)

-4.53**

(-3.95)

Negative DV X

ln(1-RE/TA)

16.76**

(12.99)

16.42**

(12.67)

16.43**

(12.69)

9.56**

(7.39)

16.11**

(12.37)

13.67**

(10.52)

1+ln(TA/TL)9.58**

(44.48)

9.27**

(40.24)

9.29**

(40.33)

9.30**

(41.83)

9.22**

(40.08)

9.22**

(40.49)

Size-0.14**

(-9.60)

-0.13**

(-8.92)

-0.13**

(-8.89)

-0.15**

(-9.13)

-0.12**

(-8.62)

-0.13**

(-8.94)

ln(CL/NCL) -0.0118**

(-3.3700)

-0.0121**

(-3.4300)

-0.0117**

(-3.3100)

-0.0014

(-0.4000)

-0.0078**

(-2.2100)

-0.0072**

(-2.0600)

Age0.0083**

(5.4500)

0.0082**

(5.0600)

0.0081**

(5.0000)

0.0008

(0.5300)

0.0083**

(5.1100)

0.0057**

(3.5100)

ln(PMI)24.91**

(14.08)

44.40**

(25.10)

Real money

supply growth

-25.39**

(-11.69)

-3.39

(-1.45)

Real lending

rate

107.13**

(12.59)

-10.51

(-1.07)

PBV ratio-3.08**

(-28.25)

Dummies

Industry N Y Y Y Y Y

Seasonal N N Y Y Y Y

Quarterly N N N Y N N

No. of obs. 28,823 28,823 28,823 28,823 28,823 28,823

R-squared 17.7% 17.9% 18.0% 23.9% 18.5% 20.1%

Dependent variable: logit function of probability of default (specifications 1-6)

27

Borrowing costs as dependent variable

A reasonable objection to our approach might be that market-based default probabilities are

estimated using models that rely on assumptions, especially related to the distribution of asset

values. Might not a better approach rely on observable indicators of default risk, such as

bond spreads or borrowing costs? We tested this assertion using (16) and a borrowing rate-

based indicator of the probability of default as the dependent variable. This firm-specific

borrowing rate variable is calculated as the gross interest expense divided by total interest-

bearing liabilities using balance sheet data from WIND. We then converted this borrowing

rate into a default probability by assuming a zero recovery rate (an assumption that affects

mainly the constant term in the regression). If borrowing rates contain useful information for

credit risk, we should expect to find correctly-signed and statistically significant coefficients

on the firm-specific variables.

We estimated variants of (16) for an identical but smaller sample of firms using the

borrowing rate- and market based-default probabilities. The availability of effective

borrowing costs reduced our sample to 1,969 firms. The results are shown in Table 11. We

find that the estimated coefficients in the borrowing rate specifications are either

“incorrectly” signed, smaller in size, or statistically insignificant. For example, profitability

appears to have little effect and higher leverage implies a lower default probability, all else

equal. In contrast to the results above, public ownership tends to lower the probability of

default, all else equal.

We conclude from these results that equity market-based default probabilities are more

effective indicators of stand-alone default risk than borrowing costs in China. We conjecture

that this reflects the contribution of implicit guarantees provided by the state to SOEs and

LGFVs. These guarantees likely benefit creditors, including banks, non-bank lenders (such as

trusts), and bond holders. Equity holders, in contrast, do not benefit from such implicit

guarantees and appear to expect that, in the event of a firm struggling to meet its obligations,

third-party support will do little to boost the value of an equity stake. Of course, by

maintaining the firm as a going concern, such bailouts ensure that the implicit call option on

asset values held by equity holders retains some value, but the effect on the value of equity is

likely to be much lower than that for debt. This is particularly true if bailouts takes the form

of increasing public ownership and a dilution of existing equity holders.

28

Table 11. Default Probabilities Pooled Regression, Q1-2006 to Q4-2014

Source: Authors’ estimates. 1/ ** represents statistically significance at 5 percent level. T-statistics in parentheses.

1 2 3 4 5 6 7 8

Constant4.84**

(79.99)

5.05**

(59.26)

5.01**

(58.18)

5.02**

(58.51)

-3.26**

(-5.17)

-3.67**

(-4.09)

-3.52**

(-3.89)

-3.43**

(-3.81)

Firm specific

variab les

ln(1-EBIT/TA)-0.08

(-0.55)

-0.01

(-0.09)

-0.02

(-0.13)

-0.02

(-0.11)

-3.60**

(-2.17)

-7.73**

(-4.45)

-7.76**

(-4.46)

-7.72**

(-4.44)

WC/TA0.69**

(12.91)

0.75**

(13.50)

0.76**

(13.74)

0.76**

(13.70)

0.91

(1.53)

-0.26

(-0.40)

-0.44

(-0.67)

-0.33

(-0.51)

ln(1-RE/TA)-0.91**

(-6.99)

-0.84**

(-6.35)

-0.84**

(-6.33)

-0.84**

(-6.34)

-8.47**

(-5.62)

-4.24**

(-2.74)

-4.26**

(-2.74)

-4.25**

(-2.74)

Negative DV X

ln(1-RE/TA)

-0.10

(-0.67)

-0.18

(-1.15)

-0.14

(-0.90)

-0.14

(-0.91)

18.97**

(11.16)

13.94**

(7.97)

13.76**

(7.82)

13.72**

(7.82)

1+ln(TA/TL)-0.57**

(-16.57)

-0.64**

(-18.29)

-0.64**

(-18.21)

-0.64**

(-18.20)

8.46**

(23.88)

8.32**

(23.05)

8.36**

(23.13)

8.30**

(22.99)

Size0.01**

(7.89)

0.01**

(9.06)

0.01**

(8.52)

0.01**

(7.87)

-0.01

(-1.84)

-0.02**

(-3.02)

-0.02**

(-3.33)

-0.01**

(-2.23)

ln(CL/NCL)-0.0025**

(-4.6900)

-0.0021**

(-3.9600)

-0.0020**

(-3.7500)

-0.0020**

(-3.7700)

-0.0057

(-1.0500)

-0.0040

(-0.7500)

-0.0048

(-0.9000)

-0.0046

(-0.8600)

Age-0.0012**

(-6.9200)

-0.0014**

(-7.9400)

-0.0015**

(-8.8600)

-0.0015**

(-8.6600)

0.0054**

(2.8900)

-0.0022

(-1.0800)

-0.0012

(-0.5700)

-0.0014

(-0.6800)

LGFV dummy

0.21**

(3.86)

-1.42**

(-2.20)

Local SOE

dummy

0.09**

(4.25)

-0.60**

(-2.41)

Central SOE

dummy

0.07**

(2.65)

0.17

(0.54)

Estimated SOE

shareholding

0.21**

(4.98)

-1.30**

(-2.65)

Dummies

Industry N Y Y Y N Y Y Y

Seasonal N Y Y Y N Y Y Y

Quarterly N Y N N N Y N N

No. of obs. 15,580 15,580 15,580 15,580 15,580 15,580 15,580 15,580

R-squared 0.078 0.158 0.160 0.160 0.096 0.143 0.144 0.144

Dependent variable: logit function of probability of default

Borrowing rate-based probability of default Market-based probability of default

29

VI. CONCLUSION

Efforts to assess default risks for Chinese firms can sometimes feel like a lost cause. At least

in the bond market, actual defaults are sufficiently rare as to provide no guidance. We find

credit spreads and borrowing costs to be largely unaffected by firm fundamentals—indeed,

creditors appear to be more focused on implicit guarantees than underlying credit quality. An

alternative but common approach until now has been to rely on descriptive statistics from the

firm-specific fundamentals as reported in financial accounts. Unfortunately, this method has

at least two shortcomings: it tells us little about the probability of default and it is backward

looking.

Can we do better? Our preliminary answer is “yes,” especially at the individual stock level.

Notwithstanding caution regarding the use of highly volatile Chinese equity market prices as

an input, we conclude that structural credit models that estimate the stand-alone 1-year

probability of default can be usefully applied in China. We find that these default

probabilities have provided signals of increased financial stress for some firms, including the

first onshore corporate bond default. We also find that these probabilities respond in

intuitively and quantitatively sensible ways to changes in a firm’s fundamentals, including

profitability and balance sheet strength. One caveat is that macroeconomic and financial

conditions appear to have less impact than might be expected; for example, investor

speculation about “policy stimulus” can offset the impact of an actual deterioration in broad

economic and financial conditions. In some sense, this forward-looking aspect of the model

should be welcomed even though there is always the possibility that equity valuations

overshoot.

30

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